good statistics project ideas

good statistics project ideas

Innovative Statistics Project Ideas

1. Introduction to Statistics Projects

The following is an in-depth list with descriptions of projects that can be implemented in a statistics course at all college levels. The level of computation needed for these examples can be used across the curriculum, but a requirement is a suitable statistical software package (such as Public Open Resource Statistical Services, abbreviated as profsss). Python is another open resource and is preferred by many students. What is important is that any project or activity can be introduced by the instructor in a very limited time, are fun, and if evaluated will be relatively easy to grade but most importantly, encourage participation back into the class.

Projects and activities during a statistics course make the class more of a ‘participant’ rather than a ‘listener’ in the learning process. But how can we get them involved? As there is only a limited number of unique examples that can be used by instructors, the attempt to seek new subject areas that can be brought into the class is a continuous battle. In this paper, I present a list of ideas ranging from small computational-based projects to StatTalks (small class mini ‘conference’ presentation unique experiences that can be done during any statistics course). These examples can be inserted into the usual statistics course as individual activities not only by the students but also by the instructor. And thus the saying, ‘There is never enough examples to use in the class’, can be eradicated.

2. Exploratory Data Analysis Projects

Some of the exploratory data analysis projects come with no guiding questions. There is a lot of room to tailor the project to your needs and skills. The best projects in a category are highlighted. With fewer than 30 observations (rows), most of them are interesting but should not be difficult. If you are interested, click to learn more about my exploratory data analysis projects. After the individual projects, there is a large collection of exploratory data analysis projects sorted by project.

Some projects require data collected specifically for the project or require the data to be combined, rearranged, or manipulated to fit your needs. For example, having calorie counts for food products would be quite helpful if you are interested in examining the variation in calorie density within a certain product category. Other projects are simple to execute and would be good for teaching. These projects have relatively complete data and require just graphing to answer the question. They are delightful as long as the lessons offered are powerful.

This post consists of ideas for exploratory data analysis projects. These are projects that you have neither the guidance nor codes available to teach you how to do them. You are more or less on your own to identify the interesting questions in the data set and share your discoveries in an analysis report. Data exploration is a form of detective work; it requires creativity and an inquiring mind.

3. Hypothesis Testing and Inference Projects

It is well known that Ivy League SAT scores approach those of football athletes’ entrance measures. If measures such as the GPA and/or ACT were available, would preferential treatment still outweigh overall scholastic performance? 8) Can daily temperatures forecast the outcome of a golf match?

Do gender and computer level skills play a role in the attendance ability of the student? 5) How can values between groups be compared when values have implied ordered grouping? 6) The relationship between GPA and ACT score with the first-year retention rate for students who graduated from high school in 1999 in the state of Oklahoma. Am I comparing like groups? 7) Demonstrate the ability by obtaining intervals to estimate the number of times the voters’ party preferences were tied in 100,000 presidential elections and compare your results to the known standard interval.

a) Class attendance during a semester is observed in the internet course regarded as an equivalent to class attendance in regards to predicting course grade score. b) Once enrolled, student characteristics which affect attendance will be related to the internet and the comparison courses. c) The final grade for internet students with excellent attendance will be closer to the comparison students’ grade than students who exhibit poor attendance.

1) Develop a statistical test to determine if two populations are significantly different over many dimensions. 2) Determine the quick and relative ease of fast food restaurant chains in various local economies in relation to poverty levels. Also, try to determine its link to fast food consumption, social habits, and obesity-related diseases. Factor in the social demographic characteristics of certain diseases. 3) How can stressful events be related to the personality of the individual? 4) Test the following hypotheses:

4. Predictive Modeling and Machine Learning Projects

So what can a data scientist do other than maintain our traditional predictive models in this wild new world? We can be creative and find new places to do more predictive modeling! This extends to both new techniques that are on the cutting edge but not quite established and using well-established methods in new areas. Broader utilization of predictive modeling and especially machine learning will make data science more powerful. With that, here are data science project ideas that involve various predictive modeling concepts. I tried to keep it broad, but need ideas that aren’t quite what you see here? PM me and I’ll help you find something cool to work on.

When people think of statistical and data analysis projects, they usually think of modeling type projects. Predictive modeling and machine learning have enjoyed a lot of attention in the last few years, and for good reason. The tool set of models a data scientist can utilize has grown significantly and so have programmatic ways for a machine to optimize its predictions. Despite this deep set of tools to predict the future, data science is mired in a false narrative that all prediction is truly done off in the future. This is categorically untrue. Predictive modeling and machine learning are needed more than ever with a trend toward automated decisions based on very basic rules. In the new automated decision system trend, a business thinks they are doing predictive modeling by doing something like no longer offering promotions over a certain discount threshold but in reality are making very simplistic, non-optimized, and sometimes biased decisions.

5. Conclusion and Future Directions

Once they open their eyes to the creative possibilities of designing a study, the biggest challenges that students might face are one of inertia or too many ideas. Have your students list 15 ideas – they probably know of possible data sources – they might not otherwise have thought to tell you – and their short-term interests will emerge. The projects will have been designed with the context in mind, ensuring work that is more realistic and more interesting.

In this piece, I have tried to lay out a way of thinking about designing statistics projects for students. In many statistical consulting courses, the number one complaint reported by students is that not understanding scientific context makes the projects uninteresting. Although I do not have lots of direct evidence, I believe that incorporating a design process for statistics projects alleviates this problem. After that, students can be allowed to flex their creative muscle, as they come up projects that interest them and that, subsequently, they are willing and able to work through challenges on. No more burying their head in the sand in terms of not understanding that the data they expect to see is not the data they actually get.